NXTGeUH: Lorawan based next generation ubiquitous healthcare system for vital signs monitoring falls detection

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dc.authorscopusid56352392700
dc.authorscopusid57200178916
dc.authorscopusid56958210200
dc.authorscopusid57205389923
dc.authorscopusid57210312647
dc.authorscopusid56644813700
dc.contributor.authorPatel, Warish D.
dc.contributor.authorPandya, Sharnil
dc.contributor.authorKoyuncu, Baki
dc.contributor.authorRamani, Bhupendra
dc.contributor.authorBhaskar, Sourabh
dc.contributor.authorGhayvat, Hemant
dc.date.accessioned2024-09-11T19:59:03Z
dc.date.available2024-09-11T19:59:03Z
dc.date.issued2018
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description1st International Conference on Data Science and Analytics, PuneCon 2018 -- 30 November 2018 through 2 December 2018 -- Pune -- 149096en_US
dc.description.abstractThe challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-The-Art outcomes in all aspects. © 2018 IEEE.en_US
dc.description.sponsorshipREACH 2020 EUen_US
dc.description.sponsorshipThe work is supported by REACH 2020 EU and EuroTech Marie Curie Fellowship.en_US
dc.identifier.doi10.1109/PUNECON.2018.8745431
dc.identifier.isbn978-153867278-5en_US
dc.identifier.scopus2-s2.0-85070277986en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/PUNECON.2018.8745431
dc.identifier.urihttps://hdl.handle.net/11363/8622
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectBody sensor networks; CNN; ECG; Emergency Alarm; Fall Detection; Healthcare; Internet of Medical Things(IoMT); LoRaWAN; Remote Health Monitoring; Telemedicine; Ubiquitous computing; Vital Signs; Wireless Sensors Network; ZIGBEEen_US
dc.titleNXTGeUH: Lorawan based next generation ubiquitous healthcare system for vital signs monitoring falls detectionen_US
dc.typeConference Objecten_US

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